Honggang Wang

Xi’an University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Honggang Wang is a researcher specializing in Radio Frequency Identification (RFID) systems and robotics, with a particular focus on improving the efficiency and intelligence of RFID-enabled robotic platforms in real-world environments. His work addresses critical challenges in automated identification systems, including tag collision resolution, dynamic system evaluation, and indoor positioning — areas with significant practical relevance in smart warehousing, logistics, and library management. Wang's most influential contribution, "Effective Anti-Collision Algorithms for RFID Robot Systems" (2019), has garnered 11 citations and tackles the persistent problem of missed readings in mobile RFID deployments, proposing algorithmic solutions that meaningfully enhance system throughput and reliability. Building on this foundation, his more recent research explores real-time system status evaluation under dynamic environmental conditions and leverages machine learning — specifically a Grey Wolf Optimizer combined with a Multi-Layer Perceptron (GWO–MLP) — to enable accurate, real-time spatial tag localization for autonomous robots. Taken together, Wang's body of work reflects a coherent research trajectory: making RFID robotic systems smarter, more adaptive, and more practically deployable. His research is particularly valuable for students and engineers working at the intersection of IoT, robotics, and intelligent automation.